[NotiAMCA] defensa Tesis L.Dalcín(CIMEC) FICH-UNL, Santa Fe // Techniques of HPDC in CFD

Mario Storti mario.storti en gmail.com
Mar Jun 24 11:24:35 ART 2008

           Defensa de Tesis de Doctorado en Ingeniería,
                  Mención Mecánica Computacional
            Facultad de Ingeniería y Ciencias Hídricas
            Universidad Nacional del Litoral Santa Fe.

Título: Techniques for High-Performace Distributed Computing in
         Computational Fluid Mechanics

Tesista:  Ing. Lisandro Dalcín

Director: Dr. Mario Storti


Jurado: Dr. Ignacio Pozoni (UN Sur y PLAPIQI, Bahía Blanca, CONICET)
        Dr. Enzo Dari (IB-UN Cuyo y CAB-CNEA, Bariloche, CONICET)
        Dres. Alberto Cardona y Jorge D'Elía
                   (CIMEC-INTEC-UN Litoral, Santa Fe, CONICET)

Fecha: Jueves 26/6/2008 16:30hs

Lugar: Sala de Conferencias Prof. Juan Carlos Alarcón.
   Facultad de Ingeniería y Ciencias Hídricas, Univ. Nacional del Litoral,
   Ciudad Universitaria. Paraje "El Pozo". S3000. Santa Fe. Argentina


 Although a lot of progress has been made in theory as well as
practice, the true costs of accessing parallel environments are still
largely dominated by software.  The number of end-user parallelized
applications is still very small, as well as the number of people
affected to their development. Engineers and scientists not
specialized in programming or numerical computing, and even small and
medium size software companies, hardly ever considered developing
their own parallelized code. High performance computing is
traditionally associated with software development using compiled
languages. However, in typical applications programs, only a small
part of the code is time-critical enough to require the efficiency of
compiled languages. The rest of the code is generally related to
memory management, error handling, input/output, and user interaction,
and those are usually the most error-prone and time-consuming lines of
code to write and debug in the whole development process. Interpreted
high-level languages can be really advantageous for these kind of

 This thesis reports the attempts to facilitate the access to
high-performance parallel computing resources within a Python
programming environment. The target audience are all members of the
scientific and engineering community using Python on a regular basis
as the supporting environment for developing applications and
performing numerical simulations. The target computing platforms range
from multiple-processor and/or multiple-core desktop computers,
clusters of workstations or dedicated computing nodes either with
standard or special network interconnects, to high-performance
shared memory machines. The net result of this effort are two open
source and public domain packages, MPI for Python (known in short as
mpi4py) and PETSc for Python (known in short as petsc4py).

 MPI for Python, is an open-source, public-domain software
project that provides bindings of the Message Passing Interface (MPI)
standard for the Python programming language. MPI for Python is a
general-purpose and full-featured package targeting the development of
parallel application codes in Python. Its facilities allow parallel
Python programs to easily exploit multiple processors. MPI for Python
employs a back-end MPI implementation, thus being immediately
available on any parallel environment providing access to any MPI

 PETSc for Python is an open-source, public-domain software project
that provides access to the Portable, Extensible Toolkit for Scientific
Computation (PETSc) libraries within the Python programming language.
PETSc for Python is a general-purpose and full-featured package. Its facilities
allow sequential and parallel Python applications to exploit state of the art
algorithms and data structures readily available in PETSc.

 MPI for Python and PETSc for Python packages are fully integrated to
PETSc-FEM, an MPI and PETSc based parallel, multiphysics, finite
elements code. Within a parallel Python programming environment,
this software infrastructure supported research activities related
to the simulation of electrophoretic processes in microfluidic
chips. This work is part of a multidisciplinary effort oriented to
design and develop these devices in order to improve current
techniques in clinical analysis and early diagnosis of cancer.

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